Moment invariants are powerful function descriptors. They have been well studied and often used on scalar fields because they are robust, intuitive, and able to represent objects independent from their specific position, orientation, and scale. Their extension to vector valued functions forms a valuable foundation for several flow visualization tasks, like pattern detection, feature categorization,
and clustering.
Advances in modern digital imaging methods are revolutionizing a wide range of scientific disciplines by facilitating the acquisition of huge amounts of data that allow the visualization,measurement, reconstruction, and archiving of complex, multi-dimensional images. At the same time, advances in computing technologies have enabled the deployment of tremendous computing resources, enabling numerical modeling of a broad gamut of scientific phenomena,and resulting in the production of vast quantities of numerical data. These data are just the starting point for the scientific exploration that modern computational and visualization methods enable. But these advanced data generation capabilities come at a cost: with increasing data size and complexity, a premium is now placed on the development of more efficient acquisition and analysis methods. In this lecture, Dr. Frank will discuss how this new paradigm of imaging as exploration is manifest and how the increasing generality of our analysis approaches has led to very general method for data analysis applicable to such disparate fields as brain imaging and severe weather.
Posted by: Deb Zemek